Fluorescent fiberoptic probe for tissue health discrimination

ABSTRACT

A system and method for the in situ discrimination of healthy and diseased tissue. A fiberoptic based probe is employed to direct ultraviolet illumination onto a tissue specimen and to collect the fluorescent response radiation. The response radiation is observed at three selected wavelengths, one of which corresponds to an isosbestic point. In one example, the isosbestic point occurs at about 431 nm. The intensities of the observed signals are normalized using the 431 nm intensity. A score is determined using the ratios in a discriminant analysis. The tissue under examination is resected or not, based on the diagnosis of disease or health, according to the outcome of the discriminant analysis.

This application is a continuation of U.S. patent application Ser. No.10/192,836, filed Jul. 10, 2002, and issued as U.S. Pat. No. 6,768,918on Jul. 27, 2004 which is incorporated by reference herein in itsentirety.

GOVERNMENT RIGHTS

This invention was made with government support under a Small BusinessInnovation Research Grant (Contract # 1R43CA75773-01) awarded by theDepartment of Health and Human Services. The government may have certainrights in the invention.

FIELD OF THE INVENTION

This invention relates generally to diagnosis of disease. Moreparticularly, the invention relates to in vivo diagnosis by opticalmethods.

BACKGROUND OF THE INVENTION

Colonic polyps appear as two major types, neoplastic and non-neoplastic.Non-neoplastic polyps are benign with no direct malignant potential anddo not necessarily need to be resected. Hyperplastic polyps, juvenilepolyps, mucosal prolapse and normal mucosal polyps are examples ofnon-neoplastic polyps. Conversely, neoplastic polyps are pre-malignant,a condition requiring resection and further surveillance. Examples ofpremalignant neoplastic polyps are tubular adenoma, villous adenoma andtubulovillous adenoma.

Conventional laser-induced fluorescence emission and reflectancespectroscopy can distinguish between neoplastic and non-neoplastictissue with accuracies approaching about 85%. However, typically thesemethods require that the full spectrum be measured with algorithmsdependent on many emission wavelengths.

SUMMARY OF THE INVENTION

The invention provides in vivo diagnostic methods based upon thenormalized intensity of light emitted from tissue. In particular, it isan observation of the invention that relevant diagnostic information isprovided by comparing the intensities of light emitted from a tissue attwo different wavelengths, both normalized over the intensity of lightemitted from the same tissue at about 431 nm.

Thus, according to the invention, a comparison of the intensities of twodifferent wavelengths normalized using the intensity at about 431 nmprovides diagnostic insight. Preferred methods of the invention compriseobtaining a fluorescent emission having a first intensity at a firstwavelength and a second intensity at a wavelength; normalizing the firstand second intensities with respect to an intensity at a wavelength ofabout 431 nm to produce first and second normalized intensities; anddetermining a state of health of the tissue based upon a comparison ofthe first and second normalized intensities.

In one embodiment, methods of the invention comprise determining thestate of health of the tissue using a classifier function in which thefirst and second normalized intensities are inputs. In one embodiment,the classifier function is a discrimination function, preferably alinear discrimination function. In other embodiments, the discriminationfunction is a non-linear discrimination function.

The invention can be applied to analyze a broad range of tissues.Preferably, the tissue to be analyzed is a tissue comprising epithelialcells. In one embodiment, the tissue is selected from the groupconsisting of cervical tissue, colonic tissue, esophogeal tissue,bladder tissue, and bronchial tissue.

Classifying or comparing normalized intensities into one or more groupsmay be performed by any acceptable means. There are numerous acceptableapproaches to such classifications. For example, one general method ofgrouping the two normalized intensities is a Bayesian-based classifierusing Mahalanobis distances. The Mahalanobis distance is well-known instatistical analysis, and is used to measure a distance between data ina multidimensional space based on characteristics that represent adegree of relationship among the data. Bayesian probabilities have beenknown in statistical analysis for many years. Specific BayesianMahalanobis-based classifier can be selected from linear discriminantanalysis, quadratic discriminant analysis, and regularized discriminantanalysis. As those familiar with statistical analysis will recognize,linear discrimination analysis and quadratic discriminant analysis aremethods that are computationallv efficient. Regularized discriminantanalysis uses a biasing method based on two parameters to estimate classcovariance matrices.

Other ways of comparing the normalized intensities include a binary treeclassifier, and an unsupervised learning cluster classifier.Unsupervised learning is characterized by the absence of explicitexamples showing what an input/output relation should be. Examples of anunsupervised learning cluster classifier include hierarchical clusteringanalysis, principal component analysis, fuzzy c-means analysis, andfuzzy k-means analysis. Each of the forgoing analytical techniques iswell known in the statistical analysis literature. For example, thefuzzy c-means algorithm divides a data set having an integer number ndata points into an integer number c fuzzy clusters, where n>c, whiledetermining a location for each cluster in a multi-dimensional space.

In another aspect, the invention features systems for determining thestate of health of a tissue. Systems of the invention comprise anillumination source for illuminating a tissue; a detector for receivingfrom the tissue light comprising a first intensity at a first wavelengthand a second intensity at a second wavelength; a computational modulefor normalizing the first and second intensities with respect toreceived light having an intensity at a wavelength of about 431 nm toproduce first and second normalized intensities; and an analysis modulefor determining a state of health of the tissue based upon a comparisonof the first and second normalized intensities.

In a preferred embodiment, a system of the invention comprises anoptical fiber as the illumination source. The detector may receive lightfrom the tissue by way of a plurality of optical fibers. In a preferredembodiment, at least one of the optical fibers of the system is placeddirectly in contact with tissue. Preferably, the light received from thetissue is fluorescent light. The analysis module of a system of theinvention may comprise a Bayesian Mahalanobis-based classifier function.The Bayesian Mahalanobis-based classifier may be selected from the groupconsisting of linear discriminant analysis, quadratic discriminantanalysis, and regularized discriminant analysis. The analysis module mayalso comprise a binary tree classifier function or an unsupervisedlearning cluster classifier. In some embodiments, the unsupervisedlearning cluster classifier is selected from the group consisting ofhierarchical clustering analysis, principal component analysis, fuzzyc-means analysis, and fuzzy k-means analysis.

Systems and methods of the invention are useful in examining a tissuecomprising epithelial cells. A method of the invention compriseslaser-induced fluorescence using light around 337 nm and a thresholdclassification model that depends on two fluorescence intensity ratiosnormalized by the intensity of fluorescence at about 431 nm.

The invention enables determining whether a polyp is neoplastic. Systemsand methods of the invention enable such determination at the time ofendoscopy particularly for diminutive polyps. In a preferred embodiment,the invention provides for identification of polyps (or other features)under about 10 mm in size. In a further preferred embodiment, theinvention provides for identification of polyps (or other features)under about 10 mm in size in real time.

The combination of a new design of a fiberoptic probe for makingmeasurements, an analytic method based on a small number of data points,and a simple method of obtaining a normalization factor for the dataused provides enhanced diagnostic accuracy in distinguishing betweenneoplastic and non-neoplastic polyps.

The invention provides methods that reliably distinguish betweenneoplastic and non-neoplastic tissue at the time of endoscopy,colonoscopy, colposcopy, or other similar examinations. As a result,patients with non-neoplastic lesions are not subjected to the risk,discomfort and expense of biopsies or excisions. Patients withneoplastic lesions can be identified immediately and treated.

The foregoing and other objects, aspects, features, and advantages ofthe invention will become more apparent from the following descriptionand from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and features of the invention can be better understood withreference to the drawings described below. The drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the invention. In the drawings, likenumerals are used to indicate like parts throughout the various views.

FIG. 1 is a schematic diagram showing an embodiment of the apparatusaccording to principles of the invention;

FIG. 2 is a plot of the normalized fluorescence spectra of normal colon,neoplastic polyps and non-neoplastic polyps showing a quasi-isosbesticpoint at 431 nm, according to an embodiment of the invention;

FIG. 3 is a flow diagram showing the steps of the analytical methodaccording to principles of the invention; and

FIG. 4 is a graph showing polyp classification results obtained using alinear discriminant analysis according to principles of the invention.

DETAILED DESCRIPTION

In one aspect, the invention utilizes the intensity of fluorescenceobserved at an isosbestic-like point as a point for normalization. Anisosbestic point is a point in wavelength space (or its equivalent) atwhich a multi-component system exhibits a constant absorbanceindependent of the relative proportions of the components. Followingnormalization to peak fluorescence intensity, polyp fluorescence spectraexhibits nearly constant fluorescence intensities at 431 nm. A preferredisosbestic-like point for use in methods of the invention is 431 nm.

According to the invention, normalizing polyp fluorescence spectra to anisosbestic point is nearly equivalent to normalizing to their peakintensities. Generally, the invention involves illuminating a specimenand observing the intensity of responsive light at each of first andsecond wavelengths. These intensities are normalized with respect to theintensity of light at an isosbestic point. Normalized intensities aretypically obtained by dividing an intensity of responsive light at awavelength by the intensity of light at the isosbestic wavelength. In apreferred embodiment, the fluorescence isosbestic point occurs at awavelength of about 431 nm.

The first and second wavelengths may be conveniently selected inaccordance with a discrimination function analysis, which is describedbelow in greater detail. The normalized responses are used at inputvalues for the discrimination function analysis. The output of thediscrimination function analysis is an indication that the specimenexamined is healthy or is diseased.

The discrimination analysis can be linear or nonlinear. In generalterms, the discrimination function is a mathematical relationship thatis constructed in at least two-dimensional space. The mathematicalrelationship is constructed in relation to groupings of observationscorresponding to one or more known medical conditions as compared toobservations corresponding to another known condition (e.g., healthy).The discrimination function used in methods of the invention is amathematical representation of one or more boundaries that separateobservations obtained from the sample being interrogated from thosecorresponding to one or more groups associated with a known condition.As is appreciated by the skilled artisan, numerous discriminationtechniques are available for application of the invention. Numerous suchtechniques are discussed below.

EXAMPLE 1

In one embodiment, the invention is practiced by illuminating tissuewith 337 nm excitation light delivered via a single optical fiber. Lightthat is remitted is collected with a plurality of optical fiberssurrounding the illumination fiber. In one embodiment, signals from theindividual collection fibers can be averaged into a single spectrumthereby increasing sensitivity. In an alternative embodiment, thesignals from the individual collection fibers can be analyzed asdiscrete signals, for example, by comparing the different signal todetermine an extent of tissue that provides a particular response.

An exemplary apparatus 100 used in this embodiment of the invention isshown in FIG. 1. The apparatus 100 includes a source 110 of 337 nmillumination as the excitation source. The excitation illumination isintroduced into an optical fiber 120 for delivery to the tissue underexamination. The illumination fiber 120 can be tapered starting at about0.4 mm in diameter at the proximal end and ending at about 0.1 mm at itsdistal end. In one embodiment, a plurality of optical fibers 130 areused to collect the response signal from the tissue under examination.In the embodiment shown, six collection fibers 130 are placed in ahexagonal array about the central optical fiber 120 that carries theexcitation illumination. This geometry is termed herein the“six-around-one fiberoptic probe.” In alternative embodiments,additional hexagonal layers of fibers disposed so as to surround thecollection fibers 130. The collection fibers are about 0.1 mm indiameter. The fiberoptic catheter 140 is delivered through the accessoryport 150 of a typical endoscope 160 with the distal tip 170 gentlytouching tissue 180 to be examined. In one embodiment, the returnedlight is separated into fluorescence bands at 403, 414 and 431 nm usinga wavelength dispersive element 190 such as a spectrograph or dichroicfilter system. The width of the bands should preferably be under 5 mm.Two intensity ratios (I₄₀₃/I₄₃₁ and I₄₁₄/I₄₃₁) are then formed and usedas input values in a linear discriminant analysis (LDA) threshold modelto produce a score indicative of the health of the tissue. Treatment orfurther diagnostic procedures are based on a characteristic of thescore, such as its sign.

This invention, in one embodiment, relates to an optical probe andmethods for identifying neoplastic tissues of the colon during endoscopyor colonoscopy and of the cervix of the uterus during colposcopy as wellas cancerous and/or pre-cancerous lesions of other organs, such as theesophagus, the urinary bladder, the oral cavity, and the bronchotrachealtree. Systems and methods of the invention can be usefully employed inexamining a tissue comprising epithelial cells. A probe according to theinvention comprises a plurality of collection fibers surrounding asingle illumination fiber. Preferably, the plurality of collectionfibers is six fibers. In a preferred embodiment, at least one of theoptical fibers of the probe is placed directly in contact with tissue. Amethod of the invention comprises laser induced fluorescence using 337mm excitation and a threshold classification model that depends on twofluorescence intensity ratios normalized by the intensity offluorescence at about 431 nm. In a preferred embodiment, the intensityat about 403 nm is divided by the intensity at about 431 nm and theintensity at about 414 nm is divided by the intensity at 431 nm.

The invention enables determining whether a polyp is neoplastic. Systemsand methods of the invention enable such determination at the time ofendoscopy particularly for diminutive polyps. In an exemplaryembodiment, fluorescent intensity at frequencies other than about 403 nmand about 414 nm are observed, and are normalized by dividing by theintensity of fluorescence observed at about 431 nm.

In a preferred embodiment, the invention provides for identification ofpolyps (or other features) under about 10 mm in size. In a furtherpreferred embodiment, the invention provides for identification ofpolyps (or other features) under about 10 mm in size in real time.

Referring to FIG. 2, a plot 200 depicting a plurality of responsespectra is shown, for different tissue types illuminated with the same337 nm excitation-light. In the example shown, the spectra observedcorrespond to tissues including normal colon 210, non-neoplastic polyps220, and neoplastic polyps 230. There is a quasi-isosbestic point at 431mm. The spectra 210, 220, 230 shown in FIG. 2 were recorded with thesix-around-one fiberoptic probe.

Changes in optical properties of collagen and blood are the predominantfactors in diagnostic differentiation among normal tissue,non-neoplastic polyps, and neoplastic polyps. An algorithm that treatscollagen fluorescence, having a peak at about 403 nm in the system ofthe invention, and hemoglobin absorption, having a peak at about 414 nmfor oxyhemoglobin, is sensitive to these changes.

Collagen and blood reside underneath the superficial cellular layer. Afiberoptic geometry designed to probe deeper into tissue but not toodeep is more sensitive to changes in collagen and blood and hence indifferentiating between types of polyps. The six-around-one fiberopticprobe used according to principles of the invention probes deeper intotissue than does a single fiber system.

Interpatient variability in the intensity of fluorescent response istypically large and affects the diagnostic accuracy of techniques basedon absolute fluorescence intensities. Historically, effective diagnosticalgorithms have used some form of normalization to reduce interpatientvariability. One common approach that has been used is to preprocess thedata by normalizing the area under each fluorescence spectrum to unity.However, this approach requires that the entire fluorescence spectrum bemeasured to calculate the area to be used for the normalization factor.The necessity to record an entire spectral response simply to be able toobtain normalization data is redundant and inefficient. The inefficiencyis particularly acute if only the emissions at 1 or 2 wavelengths are tobe analyzed.

According to the invention, a quasi-isosbestic point exists at about 431nm between the fluorescence spectra of normal tissue, hyperplasticpolyps and adenomatous polyps. The quasi-isosbestic point is used as anormalization factor that provides effective normalization whilerequiring fluorescence to be measured at only one addition emissionwavelength. For other types of pre-cancerous and/or cancerous polyps,other chemical substances are involved in the progress of the disease.These chemical substances provide characteristic signals that can occurat wavelengths other than at about 403 nm and about 414 nm.

The combination of a new design of a fiberoptic probe for makingmeasurements, an analytic method based on a small number of data points,and a simple method of obtaining a normalization factor for the dataused provides enhanced diagnostic accuracy in distinguishing betweenneoplastic and non-neoplastic polyps. The efficacy of the new system andmethod is demonstrated in a single-center prospective clinical trial. Ahigher fraction of polyps were correctly classified with this technique,(e.g., accuracy=86%) when compared to other approaches. The accuracy ofthe method using two emission wavelengths is better than that obtainedin retrospective clinical trials requiring many more wavelengths. Aretrospective trial is one in which one determines the sensitivity of analgorithm that was retrospectively optimized with data in hand. Aprospective trial is one that uses a retrospectively trained algorithmin a prospective analysis of data collected after the algorithm isdefined and tested.

Analysis Method

FIG. 3 is a flow diagram 300 showing the steps of an illustrativeanalytical method as applied to the optical signals observed fromcolonic polyps. The method involves, in the example provided above,observing fluorescent intensities at about 403, about 414 and about 431mm, as shown at step 310. The ratio of the intensity at about 403 mm tothat at about 431 nm (I₄₀₃/I₄₃₁), and the ratio of the intensity atabout 414 nm to that at about 431 nm (I₄₁₄/I₄₃₁) are formed, asindicated at step 320. The two ratios are then examined by comparison toa linear discrimination function, using linear discrimination analysis(LDA), as shown at step 330. A score value greater than zero isindicative of neoplasia, while a score value less than zero indicatesnon-neoplasia. Resection can be performed, or omitted, based on thescore value that is obtained. Result 340 represents performingresection, while result 350 represents not performing resection.

Sensitivity Analysis

FIG. 4 is a graph 400 showing illustrative polyp classification resultsobtained using a linear discriminant analysis for the colonic polypexample discussed above. One hundred and fifty patients were enrolled ina prospective study in which 94 polyps were collected from 50 patients.In FIG. 4, the about 403 nm to about 431 mm fluorescence intensity ratio(I₄₀₃/I₄₃₁) was plotted along the vertical axis 402 against the about414 mm to about 431 mm ratio (I₄₁₄/I₄₃₁) plotted along the horizontalaxis 404 for a given polyp. The LDA threshold discrimination model isdepicted as the line 410 in FIG. 4 where polyps corresponding to datapoints that lie above the line 410 are classified as neoplastic polypsand polyps corresponding to data points that lie below the line 410 areclassified as non-neoplastic polyps. Using this model, 47 of 52neoplastic polyps and 34 of 42 non-neoplastic polyps were classifiedcorrectly resulting in a sensitivity and specificity of 90% and 81%,respectively. In addition, 80 of 86 normal colonic tissue sites and 3 of3 frank adenocarcinomas were correctly classified.

OTHER EXAMPLES

The apparatus of FIG. 1 is used in other exemplary systems and methodsof the invention. Specimens to be tested for diagnostic purposes areilluminated with excitation radiation, such as 337 nm illumination.Intensities of fluorescent responses at first and second wavelengths areobserved, and are normalized using an intensity of a response at anisosbestic point. In preferred embodiments, the isosbestic point occursat 431 nm. The normalized intensities are analyzed by comparison to adiscrimination function.

Various linear or non-linear discriminant functions can be devised usingthe complex relationship between tissue fluorescence measured on thesurface and the distribution of different fluorophores that exist indifferent tissue layers. The analysis is further complicated by primaryabsorption of the excitation light and secondary absorption of theemitted light at different wavelengths by the same fluorophores andother chromophores as the light used for excitation and the emittedlight propagate through scattering media such as tissue. Diagnosticsystems and methods of the invention will operate according to anon-linear discriminant when an excess and/or a deficit of one or moreof such substances is indicative of a condition of health.

Diagnostic systems and methods of the invention will operate accordingto a non-linear discriminant based on other factors as well. Forexample, in the field of colposcopy, nonlinear discriminants can varywith other factors such as the age or race of the patient or whether thepatient is pre-, peri- or post-menopausal.

Potential Cost Savings

The ability to identify and distinguish benign and malignant polyps insitu could result in substantial cost savings. In this particularexample, 39 of 94 polyps would have been spared from being resected andbiopsied, representing a 41% savings in surgical and pathology charges.However, at present there is a false negative rate of 9.6%. The longterm outcome of not resecting these polyps will need to be determined.In comparison, other techniques spared 14% of the polyps from beingbiopsied and had a false negative rate of 0.9%. If polyps greater than 5mm in the latter study are excluded from this analysis then 27% of thepolyps would not have been biopsied and the technique would have a 3.2%false negative rate.

Application to Other Tissues

The systems and methods of the invention have been described with regardto observations on colonic tissue. The invention, involving a new probedesign and analytical method, can enhance the accuracy for identifyingneoplasia in other tissues such as the cervix of the uterus, theesophagus, the urinary bladder, the oral cavity, and the bronchotrachealtree.

Equivalents

While the invention has been particularly shown and described withreference to specific preferred embodiments, it should be understood bythose skilled in the art that various changes in form and detail may bemade therein without departing from the spirit and scope of theinvention.

1. A system for determining a condition of a tissue, the systemcomprising: an illumination source for illuminating a tissue with lightat an excitation wavelength; a detector for determining a first andsecond intensity of received electromagnetic radiation from said tissueupon illumination at said excitation wavelength, wherein said firstintensity corresponds to a first wavelength and said second intensitycorresponds to a second wavelength different from said first wavelength;a computational module for normalizing said first and second intensitieswith an intensity corresponding to an isosbestic point, and fordetermining a condition of said tissue based at least in part on saidnormalized first and second intensities.
 2. The system of claim 1,wherein said excitation wavelength is 337 nm.
 3. The system of claim 1,wherein said isosbestic point is a wavelength of about 431 nm.
 4. Thesystem of claim 1, wherein said illumination source comprises an opticalfiber.
 5. The system of claim 1, wherein said detector receives lightfrom said tissue by way of a plurality of optical fibers.
 6. The systemof claim 5, wherein said detector receives fluorescent light from saidtissue by way of said plurality of optical fibers.
 7. The system ofclaim 1, wherein said computational module comprises a BayesianMahalanobis-based classifier function.
 8. The system of claim 7, whereinsaid Bayesian Mahalanobis-based classifier is selected from the groupconsisting of linear discriminant analysis, quadratic discriminantanalysis, and regularized discriminant analysis.
 9. The system of claim1, wherein said computational module comprises a binary tree classifierfunction.
 10. The system of claim 1, wherein said computational modulecomprises an unsupervised learning cluster classifier.
 11. The system ofclaim 10, wherein said unsupervised learning cluster classifier isselected from the group consisting of hierarchical clustering analysis,principal component analysis, fuzzy c-means analysis, and fuzzy k-meansanalysis.